1. Field of the Invention
The present invention generally relates to diagnostic instruments for human and veterinary applications, and more specifically relates to methods and systems for calibrating such instruments.
2. Description of the Prior Art
Several studies have been published regarding patient-based quality assurance for automated analyzers. Subsets of these studies have demonstrated that patient-based results can provide information regarding analyzer performance (see, Ye J J, Ingels S C, and Parvin C A, “Performance Evaluation and Planning for Patient-Based Quality Control Procedures,” Am J Clin Pathol 2000; 113: 240-248). The general focus of other investigations, and reasons for discarding the approach, was to qualify each patient result as a basis for what is reported (see, Norbert W. Tietz, Ed. Fundamentals of Clinical Chemistry, Third Ed. Philadelphia, Pa.: W.B. Saunders Company; 1987: 249-251). There is a fundamental flaw in that approach since a single patient result cannot be used to determine if it is appropriate to report. If the focus is changed slightly to monitor instrument-system performance using population data generated from aggregated patient results, then the approach has power and will provide reliable information regarding overall performance.
Weighted moving averages algorithms have been used since about 1974 for analysis of human hematology analyzer performance, starting with Bull's moving averages, sometimes referred to as X-B or XB (see, Bull B. S., et al., “A study of various estimators for the derivation of quality control procedures from patient erythrocyte indices”, Am. J. Clin. Pathol. 1974; Vol. 61:473-481). Since then, additional methodology has been introduced and implemented including exponentially weighted moving averages algorithms, sometimes referred to as EWMA, XM or XM (see, Beckman Coulter Bulletin 9611 2006; www.beckmancoulter.com; 1-800-352-3433). In automated hematology analyzers for human samples, fixed cell controls are commonly used to determine instrument performance and calibration settings. Weighted averages provide the benefit that the analysis is performed on patient samples run on the analyzer and fill the gap between control runs, which are usually once per shift, approximately every eight hours or more frequently as recommended by organizations such as the Clinical and Laboratory Standards Institute (CLSI, Wayne, Pa.; http://www.clsi.org/; 1-877-447-1888). The use of weighted averages can provide an early warning that results may be in question even before the time to run the next control.
Diagnostic instruments have been used for decades in both the human and veterinary markets. These instruments include hematology analyzers, blood chemistry analyzers and other instruments that determine certain physiological properties of patients. In the veterinary market, the VetTest® chemistry analyzer and the VetAutoread™ automated hematology analyzer have been available since at least the 1990's. Some analyzers, like the VetAutoread™ hematology analyzer manufactured by IDEXX Laboratories, Inc. of Westbrook, Me., (see, www.idexx.com), utilize a fixed optical reference to determine instrument performance. Other analyzers, like the IDEXX LaserCyte® hematology analyzer, incorporate polymers with fixed size and index of refraction to ensure optical performance referred to as Qualibeads™. In addition, some analyzers, like the Sysmex XT-V manufactured by Sysmex Corp. of Hyogo, Japan (see, www.sysmex.com), utilize a fixed cell control material to ensure assay performance based on guidelines provided by organizations like CLSI, such as the College of American Pathologists (CAP, Northfield, Ill.; www.cap.org) and the American Society for Veterinary Clinical Pathology (ASVCP) (see, Flatland B, Freeman K P, Friedrichs K R, et al., “ASVCP quality assurance guidelines: control of general analytical factors in veterinary laboratories,” Vet Clin Pathol 39/3 (2010) 264-277).
Human cells are generally utilized in the formulation of fixed-cell controls. These samples may require a specific (human) algorithm that can be very different from veterinary sample algorithms. Fundamentally, the control runs may be stable and accurate, but specific species responses may deviate due to chemical, fluidic, algorithmic, or other reasons. Patient-based methods provide species-specific analyses that can augment performance checks with fixed-cell controls and confirm that the system is performing accurately for each species.
As will be seen, the methods of the present invention also have potential applications in non-hematology systems. Chemistry analyzers commonly have optical references to verify system control. For laboratory quality results, many methods have been proposed to detect system failures with corresponding result qualification or disqualification. These methods often use analyte-specific control limits (see, Chembrowski, George S., “Thoughts on quality-control systems: a laboratorian's perspective,” Clinical Chemistry 43:5, 886-892, 1997). One added benefit of patient-based quality assurance is that chemistry control products are generally based on human-expected performance, which may be significantly different with non-human samples. One criticism of patient-based quality assurance for chemistry analyzers is that, unlike many hematology parameters, chemistry results can have wide reference intervals and can have significantly wider variations in clinically ill patients. Analyte specific changes in rules or batch sizes may be required. To facilitate an understanding of the invention, the description of the preferred embodiments will be primarily directed to the hematology applications.
The veterinary market is very cost sensitive and controls are not run in the same manner as in human practices, which generally run fixed cell controls approximately at least once per 8-hour shift. Therefore, the use of weighted moving averages performed on patient samples is beneficial to veterinary applications. In addition, weighted moving averages have the additional benefit in veterinary applications that expenses are covered during normal patient runs and not in extra control materials and consumable usage. Even in analyzers with fixed cell controls, the benefit from applying a moving averages algorithm to patient samples can be great since fixed cell control material analysis loses power with increasing number of patient runs and time between control runs (see, Westgard “QP-14: What's wrong with statistical quality control? —Westgard QC”; www.westgard.com).
Bull's moving averages algorithm has been used to track patient results in automated hematology analyzers for veterinary applications (see, Sysmex XT-V; www.sysmex.com; 1-800-462-1262; Siemens Advia® 120 Hematology System; www.medical.siemens.com; 1-800-888-7436; Abbott Cell-Dyne 3700; www.abbottdiagnostics.com; (847) 937-6100)). Bull's algorithm is written in the following form (see, Bull B. S., et al. “A study of various estimators for the derivation of quality control procedures from patient erythrocyte indices”, Am. J. Clin. Pathol. 1974; Vol. 61:473-481):
                                          X                          B              ,              i                                _                =                                            X                              B                ,                                  i                  -                  1                                                      _                    +                                    sgn              ⁡                              (                                                      ∑                                          j                      =                      1                                        N                                    ⁢                                                            sgn                      ⁡                                              (                                                                              X                            ji                                                    -                                                                                    X                                                              B                                ,                                                                  i                                  -                                  1                                                                                                                      _                                                                          )                                                              ⁢                                                                                                                                                X                                                          j                              ,                              i                                                                                -                                                                                    X                                                              B                                ,                                                                  i                                  -                                  1                                                                                                                      _                                                                                                                                                                      )                                      *                                                            (                                                            ∑                                              j                        =                        1                                            N                                        ⁢                                                                  sgn                        ⁡                                                  (                                                                                    X                              ji                                                        -                                                                                          X                                                                  B                                  ,                                                                      i                                    -                                    1                                                                                                                              _                                                                                )                                                                    ⁢                                                                                                                                                            X                                                              j                                ,                                i                                                                                      -                                                                                          X                                                                  B                                  ,                                                                      i                                    -                                    1                                                                                                                              _                                                                                                                                                                                      )                                2                            N                                                          (                  Eq          .                                          ⁢          1                )            
Generally, Bull's algorithm groups 20 consecutive patient results into a single Bull batch based on Equation 1. The following logic flow describes the steps used in the above equation to determine Bull batch values:                1. Determine the average of the first N=20 samples; this is the first Bull batch;        2. For each of the next N=20 samples, calculate the absolute difference between each patient result and the previous Bull batch;        3. Sum all of the values from step (2), maintaining the sign of the difference within the sum;        4. Square the result from Step (3) and divide by N;        5. Add the result from Step (4) to the previous Bull batch to define the current Bull batch; and        6. Repeat Steps (2)-(5) for all remaining Bull batch calculations.        
A graphical representation/flow chart of the steps described above in applying Bull's algorithm to generate summary batches from individual patient results is shown in FIG. 1A.
There are many benefits of utilizing Bull batches to summarize patient samples into a control chart, which is displayed on the analyzer so that the clinician may determine if the analyzer needs to be re-calibrated, even during the period between fixed cell control tests. The Bull weighted moving averages algorithm provides a means to reduce the impact of single sample variations on batch results. Also, utilizing the analysis for red cell indices, that is, RBC (red blood cells), MCV (mean corpuscular volume), HGB (hemoglobin), HCT (hematocrit), MCH (mean corpuscular hemoglobin), and MCHC (mean corpuscular hemoglobin concentration), has additional benefit since several of the parameters (MCV, MCH, and MCHC) have tight normal variations, within species, that can provide additional information with respect to result accuracy. Many concerns related to specialty practices running multiple sick patients and oncology patients can be mitigated since there are few clinical conditions that drive significant variation in MCV, MCH, and MCHC for a population of patients.
By removing runs that have clinical flags or impossible responses (such as a zero occurring from a short sample), Batch results will provide easily charted results that are not heavily weighted by outlier results that are due to patient response, sample handling, analyzer variation or the like.
More specifically, results must be qualified prior to inclusion in the batch analysis. Repeat runs on a particular patient within a batch are removed. Runs that have clinical or analyzer flags are removed. Runs with impossible responses, such as those stemming from a gross instrument error like a short sample, are removed. Batch results provide easily charted values that are not heavily weighted by outlier results due to patient response, sample handling, or analyzer variation. Outlier runs, defined as patient results that report significantly different than the normally measured population on that analyzer either due to patient response or system malfunction (FIG. 1(a)), have no significant impact on batches. Due to flagging and other internal checks, these results are often not reported to the user. The 20 sample-average batch results track very well with patient population results.
FIG. 1 shows a representation of MCHC results, both as raw patient results and as Bull batches. As stated above, outlier runs, shown in FIG. 1(a), are shown to have limited impact on Bull batches. In addition, FIG. 1(b) shows the same data set from FIG. 1(a), zooming in on the results without outliers and identifying that the Bull batches track very well with patient population results.
More specifically, FIG. 1 is a set of graphs of MCHC equine patient results with associated Bull batches for an automated hematology analyzer. FIG. 1(a), with all data included, shows little impact from outliers on Bull batches (20 runs per batch). FIG. 1(b) is a zoom in on largest population of instrument response taken from FIG. 1(a) showing that the Bull batches track the population variation.
Control chart rules are in place in many conventional hematology analyzers to provide feedback when the Bull batches show a trend or bias outside of limits. Standard Westgard Rules have been used in multiple applications of weighted moving averages in chemistry and hematology automated analyzers (see, Westgard J. O., et al. “A Multi-Rule Shewhart Chart for Quality Control in Clinical Chemistry”, Clin. Chem. 27/3, 493-501, 1981; Koch D. D. “Selection of Medically Useful Quality-Control Procedures for Individual Tests Done in a Multitest Analytical System”, Clin. Chem., Vol. 36, No 2: 230-233, 1990; Lunetzky E S Cembrowski G S, “Performance characteristics of Bull's multirule algorithm for the quality control of multichannel hematology analyzers”, American Journal of Clinical Pathology, 1987 Nov. 88(5):634-8. Rules implemented on Bull batches can have higher power than the same number of patient results, since each Bull batch corresponds to 20 patient runs. The act of grouping runs into batches that also include prior batch values provides a smoothing effect, so a rule that may otherwise require 10 points can now be utilized with far fewer points.
In most if not all conventional hematology analyzers, whether for human or veterinary applications, it is the clinician who must manually compare the control charts depicted as graphs displayed on the analyzer to determine whether the analyzer is out of calibration and needs adjustment of one of its parameters, such as optical gain, for example. This applies whether or not the control charts are derived from periodically run fixed cell controls, or from a weighted moving average applied to patient samples. To the knowledge of the inventor, no automated system or method is employed in either human hematology analyzers or veterinary hematology analyzers which monitors the performance of such analyzers based on patient samples and through feedback adjusts the parameters of the analyzer in real time to maintain the analyzer within its calibration specifications.